Alpine grassland drought monitoring method based on Parmer drought index localization verification

Abstract

The invention discloses an alpine grassland drought monitoring method based on Parmer drought index localization verification, which is applied to the field of meteorological drought monitoring, and comprises the following steps: calculating the day-by-day meteorological drought index of the alpine grassland based on the average day-by-day rainfall data of the alpine grassland, and combining with the average day-by-day soil humidity data, and according to the drought grade division result, calculating the weather drought index of the alpine grassland; calculating the drought monitoring precision of different meteorological drought indexes in the alpine grassland to obtain a to-be-verified Parmer drought index which is most suitable for drought monitoring of the alpine grassland; analyzing the correlation between the Pearson drought index and the soil humidity by using a Pearson correlation coefficient method, constructing a linear regression model of the Pearson drought index and the soil humidity, fitting data by using a linear fitting algorithm, minimizing the difference between a predicted value and an actual value, calculating to obtain a coefficient in the linear regression model, and calculating the soil humidity. And verifying the Parmer drought index according to the coefficient, and carrying out drought monitoring on the alpine grassland. The drought monitoring precision of the alpine grassland is effectively improved.

Description

Technical Field

[0001] The invention relates to the field of meteorological drought monitoring, and in particular to a high-cold grassland drought monitoring method based on localized verification of the Palmer Drought Index. Background Art

[0002] The global arid and semi-arid climate zones are widely distributed, accounting for more than 30% of the land area. As an extreme climate event, the increase in the frequency and intensity of drought has a significant impact on the global ecosystem. Drought not only leads to reduced agricultural production and water shortages, but also aggravates land degradation and desertification, thereby threatening biodiversity and ecological balance. Due to its unique geographical and climatic conditions, the Qinghai-Tibet Plateau is highly sensitive to climate change and is an important indicator of global climate change. The alpine grassland in this area is one of the most extensive ecosystems on the earth and is of great significance for maintaining ecological balance and biodiversity. However, due to the frequent occurrence of drought events, the ecosystem of the Qinghai-Tibet Plateau is facing serious threats. Studies have shown that under extreme drought conditions, the net primary productivity of alpine grasslands decreases by an average of 47%, which has a significant impact on the stability of the ecosystem. Drought not only restricts vegetation growth and weakens water conservation capacity, but also increases surface runoff and reduces groundwater recharge, directly affecting the water supply in the Yellow River Basin. These changes not only affect the balance of the local ecosystem, but also pose a threat to the livelihoods of herders and economic development. Therefore, studying the alpine grassland drought monitoring index will help improve our understanding of global climate change and provide a scientific basis for formulating effective ecological protection and sustainable development policies.

[0003] Meteorological drought index is an important tool for evaluating the intensity and scope of drought, and can provide fast and accurate drought monitoring information. Currently, commonly used drought indices include precipitation anomaly percentage (Pa), standardized precipitation index (SPI), comprehensive meteorological drought index (CI), and Palmer drought index (PDSI). Different drought indices show spatiotemporal differences in different regions, and their responses to drought are inconsistent. For example, Yang Qing et al. pointed out that scPDSI automatically adjusts its internal parameters to better adapt to the climatic conditions of different regions. Compared with traditional PDSI and modified PDSI (PDSI_CN), it provides more accurate drought monitoring in a wide range of regions. However, due to the vast territory and great geographical and climatic differences, some indices are less applicable in special regions such as the Qinghai-Tibet Plateau and other extreme climate and terrain ranges. In addition, the formation of drought is a complex natural process, which is affected by multiple factors such as precipitation patterns, soil moisture, temperature changes and terrain. Therefore, it is necessary to evaluate the adaptability of the index for specific regions. Since these factors show significant heterogeneity in different regions, it is difficult to find a drought index that is universally applicable to all regions. This makes it particularly important to conduct regional drought index calibration. Different drought indices have obvious differences in performance in arid and semi-arid areas, and regional calibration can significantly improve the accuracy of drought prediction. Some scholars have used the cumulative frequency method to localize the CI index and verified its effectiveness in specific regions. In recent years, although there have been some progress in the evaluation and application of meteorological drought indices, drought monitoring research in alpine grasslands, especially in the Qinghai-Tibet Plateau, is still insufficient.

[0004] Therefore, how to provide a method for drought monitoring in alpine grasslands based on localized calibration of the Palmer Drought Index that can effectively improve the drought monitoring accuracy in alpine grasslands is an urgent problem that technicians in this field need to solve. Summary of the invention

[0005] In view of this, the present invention proposes a method for monitoring drought in alpine grasslands based on localized verification of the Palmer Drought Index.

[0006] In order to achieve the above object, the present invention adopts the following technical solution:

[0007] A method for monitoring drought in alpine grasslands based on localized verification of the Palmer Drought Index, comprising:

[0008] Step 1: Obtain historical precipitation data and historical soil moisture data of alpine grassland, perform preprocessing, and calculate the average daily precipitation data and average daily soil moisture data;

[0009] Step 2: Calculate the daily meteorological drought index of alpine grassland based on the average daily precipitation data, and combine it with the average daily soil moisture data. According to the drought grade classification results, calculate the drought monitoring accuracy of different meteorological drought indices in alpine grassland, and obtain the Palmer drought index to be verified that is most suitable for drought monitoring in alpine grassland; the meteorological drought index includes: precipitation anomaly percentage, standardized precipitation index, comprehensive meteorological drought index and Palmer drought index;

[0010] Step 3: Use the Pearson correlation coefficient method to analyze the correlation between the Palmer Drought Index and soil moisture, construct a linear regression model of the Palmer Drought Index and soil moisture, and use a linear fitting algorithm to fit the data to minimize the difference between the predicted value and the actual value. Calculate the coefficients in the linear regression model, calibrate the Palmer Drought Index based on the coefficients, and conduct drought monitoring in alpine grasslands.

[0011] Optionally, in step 1, preprocessing includes: removing outliers and filling out outliers with the mean.

[0012] Optionally, in step 2, based on the daily meteorological drought index of the alpine grassland and combined with the average daily soil moisture data, according to the drought grade classification results, the drought monitoring accuracy of different meteorological drought indices in the alpine grassland is calculated as follows:

[0013]

[0014] Among them, O s is the drought monitoring accuracy; d is the number of samples identified as drought in drought monitoring based on both meteorological drought index and soil moisture data; s is the number of samples in which the meteorological drought index is identified as no drought and the soil moisture data is identified as drought in the drought monitoring based on the meteorological drought index and soil moisture data; L b is the number of samples identified as drought-free in drought monitoring based on meteorological drought index and soil moisture data; L s It is the number of samples in which the meteorological drought index is identified as drought and the soil moisture data is identified as no drought in drought monitoring based on the meteorological drought index and soil moisture data.

[0015] Optionally, in step 2, the Palmer Drought Index is calculated as follows:

[0016]

[0017] in, The climate has suitable precipitation; For climate suitable heat dissipation; Provide water for the right climate; The amount of water loss is suitable for the climate; The runoff volume is suitable for the climate;

[0018]

[0019] Where d is the moisture anomaly compared to the normal value; P is the precipitation in a certain period;

[0020] Z = dK;

[0021] Among them, Z is the moisture anomaly index; K is the climate weight coefficient of a given month at a specified location;

[0022]

[0023] Where X is the Palmer Drought Index.

[0024] Optionally, in step 3, the Pearson correlation coefficient method is as follows:

[0025]

[0026] Where cov(X,Y) is the covariance of X and Y; std(X) and std(Y) are the standard deviations of X and Y, respectively. Optionally, in step 3, the linear regression model of the Palmer Drought Index and soil moisture is as follows:

[0027] Y = β0 + β1·X;

[0028] Among them, Y is soil moisture; β0 is the intercept; β1 is the regression coefficient; X is the optimal meteorological drought index.

[0029] Optionally, in step 3, the linear fitting algorithm includes but is not limited to the least squares method.

[0030] Optionally, in step 3, the Palmer Drought Index is calibrated according to the coefficients as follows:

[0031]

[0032] Where X' is the calibrated Palmer Drought Index; X is the Palmer Drought Index; β0 is the intercept; and β1 is the regression coefficient.

[0033] It can be seen from the above technical solution that, compared with the prior art, the present invention proposes a method for monitoring drought in alpine grasslands based on the localized verification of the Palmer Drought Index. By comparing the recognition accuracy of the precipitation deviation percentage (Pa), the standardized precipitation index (SPI), the comprehensive meteorological drought index (CI) and the Palmer Drought Index (PDSI) in alpine land, the meteorological drought index PDSI to be verified that is most suitable for alpine land is selected, and a linear regression model of PDSI and soil moisture is further established. The linear fitting algorithm is used to minimize the difference between the predicted value and the actual value, and the linear regression model coefficient is obtained to further localize the PDSI. Experiments show that the localized index PDSI r can more accurately capture the spatial variability of drought conditions than PDSI, and effectively improves the drought monitoring accuracy of alpine grasslands. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0035] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0036] Figure 2 This is a schematic diagram of the standard drought situation distribution of the PDSI index in 2022 of the present invention.

[0037] Figure 3 This is a schematic diagram of the standard drought situation distribution of the PDSIr index in 2022 of the present invention.

[0038] Figure 4 This is a schematic diagram of soil moisture and drought level in each month of 2022 according to the present invention. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Embodiment 1:

[0041] Embodiment 1 of the present invention discloses a method for monitoring drought in alpine grasslands based on localized verification of the Palmer Drought Index. Figure 1 As shown, including:

[0042] Step 1: Obtain historical precipitation data and historical soil moisture data of alpine grassland, perform preprocessing, and calculate the average daily precipitation data and average daily soil moisture data.

[0043] Take the precipitation data and soil moisture data of Maqu County from 2000 to 2020 as an example. The historical precipitation data is taken from the precipitation data in the TerraClimat dataset of GEE meteorological data interpolation data; the historical soil moisture data is derived from the SMCI1.0 dataset, taking the soil moisture data at soil depths of 20 cm and 30 cm.

[0044] Preprocessing, including: removing outliers and filling them with the mean.

[0045] The average daily precipitation data is the average of the precipitation data on the same day of each year in the sample; the average daily soil moisture data is the average of the soil moisture data on the same day of each year in the sample. The soil moisture data averaged on the same day here includes both soil moisture data at a depth of 20 cm and soil moisture data at a depth of 30 cm.

[0046] The soil moisture data in the embodiment of the present invention adopts the soil moisture drought classification standard in "GBT204812006 Meteorological Drought Grade", which divides the soil moisture value into five levels: no drought, light drought, moderate drought, severe drought and extreme drought. The specific classification standards are:

[0047] No drought: soil moisture > 60%;

[0048] Mild drought: 50%<soil moisture≤60%;

[0049] Moderate drought: 40%<soil moisture≤50%;

[0050] Severe drought: 30%<soil moisture≤40%;

[0051] Extreme drought: soil moisture ≤30%.

[0052] Step 2: Calculate the daily meteorological drought index of alpine grassland based on the average daily precipitation data, and combine it with the average daily soil moisture data. According to the drought level classification results, calculate the drought monitoring accuracy of different meteorological drought indices in alpine grassland, and obtain the Palmer Drought Index to be verified that is most suitable for drought monitoring in alpine grassland; among them, meteorological drought index includes: precipitation deviation percentage, standardized precipitation index, comprehensive meteorological drought index and Palmer Drought Index.

[0053] The present invention selects the Palmer Drought Index as the index calibration object for alpine grasslands based on the comparison of drought monitoring accuracy of meteorological drought indices such as precipitation anomaly percentage (Pa), standardized precipitation index (SPI), comprehensive meteorological drought index (CI) and Palmer Drought Index (PDSI) in alpine grasslands. The specific process is as follows:

[0054] Based on the average daily precipitation data, the precipitation anomaly percentage (Pa), standardized precipitation index (SPI), comprehensive meteorological drought index (CI) and Palmer drought index (PDSI) of the alpine grassland were calculated. Combined with the average daily soil moisture data, according to the drought grade classification results, the drought monitoring accuracy of different meteorological drought indices in the alpine grassland was calculated to compare the drought monitoring accuracy; the drought monitoring accuracy of different meteorological drought indices in the alpine grassland was calculated as follows:

[0055]

[0056] Among them, O s is the drought monitoring accuracy; d is the number of samples identified as drought in drought monitoring based on both meteorological drought index and soil moisture data; s is the number of samples in which the meteorological drought index is identified as no drought and the soil moisture data is identified as drought in the drought monitoring based on the meteorological drought index and soil moisture data, i.e., missed detection; L b is the number of samples identified as drought-free in drought monitoring based on meteorological drought index and soil moisture data; L s It is the number of samples in which the meteorological drought index is identified as drought and the soil moisture data is identified as no drought in drought monitoring based on the meteorological drought index and soil moisture data, that is, multiple measurements.

[0057] Finally, the recognition accuracy of the precipitation anomaly percentage (Pa) of alpine grassland was 48.21%, the recognition accuracy of the standardized precipitation index (SPI) was 38.79%, the recognition accuracy of the comprehensive meteorological drought index (CI) was 37.42%, and the drought monitoring accuracy of the Palmer Drought Index (PDSI) was 59.16%.

[0058] The Palmer Drought Index is calculated as follows:

[0059]

[0060] in, The climate has suitable precipitation; For climate suitable heat dissipation; Provide water for the right climate; The amount of water loss is suitable for the climate; The runoff volume is suitable for the climate;

[0061]

[0062] Where d is the moisture anomaly compared to the normal value; P is the precipitation in a certain period;

[0063] Z = dK;

[0064] Among them, Z is the moisture anomaly index; K is the climate weight coefficient of a given month at a specified location;

[0065]

[0066] Where X is the Palmer Drought Index.

[0067] Pearson correlation coefficient method, as follows:

[0068]

[0069] Among them, cov(X,Y) is the covariance of X and Y; std(X) and std(Y) are the standard deviations of X and Y. The linear regression model of the Palmer Drought Index and soil moisture is as follows:

[0070] Y = β0 + β1·X;

[0071] Among them, Y is soil moisture; β0 is the intercept; β1 is the regression coefficient; X is the optimal meteorological drought index.

[0072] Step 3: Use the linear fitting algorithm to fit the data, minimize the difference between the predicted value and the actual value, calculate the coefficients in the linear regression model, and calibrate the Palmer Drought Index based on the coefficients to monitor drought in alpine grasslands.

[0073] Linear fitting algorithms include, but are not limited to, the least squares method.

[0074] The Palmer Drought Index is calibrated according to the coefficients as follows:

[0075]

[0076] Among them, X' is the calibrated Palmer Drought Index; x is the Palmer Drought Index; β0 is the intercept; β1 is the regression coefficient.

[0077] Embodiment 2:

[0078] Embodiment 2 of the present invention discloses a specific application of a method for monitoring drought in alpine grasslands based on localized verification of the Palmer Drought Index, as follows:

[0079] Taking 2022 as a typical drought year, PDSI and PDSI r are used to monitor drought in the eastern edge of the Qinghai-Tibet Plateau in time and space, and analyze the spatiotemporal distribution and seasonal characteristics of drought. In the process of spatiotemporal variation of drought, the localized index PDSIr can more accurately capture the spatial variability of drought. Specifically, PDSI interpolation images, such as Figure 2 As shown in Figure 1, August was mainly characterized by mild and moderate drought, but there were dark-colored extreme drought pixels; there were drought pixels in September and October; the PDSI r interpolation image, such as Figure 3 As shown in Figure 2, the extremely drought pixels were removed in August, and both September and October showed no drought, which is consistent with the following Figure 4 The situation based on the measured data of soil moisture data is shown. Throughout 2022, the drought was most severe in the southeastern part of the study area, mainly distributed in the low-altitude flat area between 3300-3700 meters, where precipitation concentration and evaporation are large, exacerbating the drought. The western and northern regions are relatively mild in drought due to their high terrain, high precipitation and low evaporation rate.

[0080] In addition, the error statistics of drought pixels of PDSIr from January to December 2022 are shown in Table 1. The PDSIr index is more sensitive to the identification of drought-free conditions, such as the error of 2.19% in January, and the error of drought-free conditions throughout the year is between 1.65% and 16.62%. The number of drought conditions identified by PDSIr is generally lower than that of PDSI, such as the error of 0.47% in January. In September and October, PDSIr is more accurate in identifying drought, and the accuracy is significantly improved due to the removal of most drought pixels.

[0081] Table 1 Statistics of drought pixel errors of PDSIr from January to December 2022

[0082] month No drought error (%) Drought error (%) 1 2.19 0.47 2 6.45 1.46 3 2.36 0.98 4 7.97 8.47 5 1.65 0.82 6 10.38 10.83 7 7.01 5.64 8 6.4 4.3 9 16.62 125.66 10 16.56 3746.97 11 1.19 0.55 12 13.16 2.94

[0083] The embodiment of the present invention discloses a method for monitoring drought in alpine grasslands based on the localized verification of the Palmer Drought Index. By comparing the recognition accuracy of the precipitation deviation percentage (Pa), the standardized precipitation index (SPI), the comprehensive meteorological drought index (CI) and the Palmer Drought Index (PDSI) in alpine land, the meteorological drought index PDSI to be verified that is most suitable for alpine land is selected, and a linear regression model of PDSI and soil moisture is further established. The linear fitting algorithm is used to minimize the difference between the predicted value and the actual value, and the linear regression model coefficient is obtained to further localize the PDSI. Experiments show that the localized index PDSI r can more accurately capture the spatial variability of drought conditions than PDSI, and effectively improves the drought monitoring accuracy of alpine grasslands.

[0084] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0085] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring drought in alpine grasslands based on localized verification of the Palmer Drought Index, characterized in that: include: Step 1: Obtain historical precipitation data and historical soil moisture data of alpine grassland, perform preprocessing, and calculate the average daily precipitation data and average daily soil moisture data; Step 2: Calculate the daily meteorological drought index of the alpine grassland based on the average daily precipitation data, and calculate the drought monitoring accuracy of different meteorological drought indices in the alpine grassland according to the drought grade classification result in combination with the average daily soil moisture data, and obtain the Palmer drought index to be verified that is most suitable for drought monitoring in the alpine grassland; wherein the meteorological drought index includes: precipitation deviation percentage, standardized precipitation index, comprehensive meteorological drought index and Palmer drought index; Step 3: Use the Pearson correlation coefficient method to analyze the correlation between the Palmer Drought Index and the soil moisture, construct a linear regression model of the Palmer Drought Index and the soil moisture, and use a linear fitting algorithm to fit the data to minimize the difference between the predicted value and the actual value, calculate the coefficients in the linear regression model, calibrate the Palmer Drought Index based on the coefficients, and conduct drought monitoring of the alpine grassland.

2. The method for monitoring drought in alpine grasslands based on localized verification of the Palmer Drought Index according to claim 1, characterized in that: In step 1, the preprocessing includes: removing outliers and filling out outliers with the mean.

3. The method for monitoring drought in alpine grasslands based on localized verification of the Palmer Drought Index according to claim 1, characterized in that: In step 2, based on the daily meteorological drought index of the alpine grassland and combined with the average daily soil moisture data, according to the drought grade classification result, the drought monitoring accuracy of different meteorological drought indices in the alpine grassland is calculated as follows: Among them, O s is the drought monitoring accuracy; d is the number of samples identified as drought in drought monitoring based on both meteorological drought index and soil moisture data; s is the number of samples in which the meteorological drought index is identified as no drought and the soil moisture data is identified as drought in the drought monitoring based on the meteorological drought index and soil moisture data; L b is the number of samples identified as drought-free in drought monitoring based on meteorological drought index and soil moisture data; L s It is the number of samples in which the meteorological drought index is identified as drought and the soil moisture data is identified as no drought in drought monitoring based on the meteorological drought index and soil moisture data.

4. The method for monitoring drought in alpine grasslands based on localized verification of the Palmer Drought Index according to claim 1, characterized in that: In step 2, the Palmer Drought Index is calculated as follows: in, The climate has suitable precipitation; For climate suitable heat dissipation; Provide water for the right climate; The amount of water loss is suitable for the climate; The runoff volume is suitable for the climate; Where d is the moisture anomaly compared to the normal value; P is the precipitation in a certain period; Z = dK; Among them, Z is the moisture anomaly index; K is the climate weight coefficient of a given month at a specified location; Where X is the Palmer Drought Index.

5. The method for monitoring drought in alpine grasslands based on localized verification of the Palmer Drought Index according to claim 1, characterized in that: In step 3, the Pearson correlation coefficient method is as follows: Where cov(X,Y) is the covariance of X and Y; std(X) and std(Y) are the standard deviations of X and Y, respectively.

6. The method for monitoring drought in alpine grasslands based on localized verification of the Palmer Drought Index according to claim 1, characterized in that: In step 3, the linear regression model of the Palmer Drought Index and the soil moisture is as follows: Y = β0 + β1·X; Among them, Y is soil moisture; β0 is the intercept; β1 is the regression coefficient; X is the optimal meteorological drought index.

7. The method for monitoring drought in alpine grasslands based on localized verification of the Palmer Drought Index according to claim 1, characterized in that: In step 3, the linear fitting algorithm includes but is not limited to the least squares method.

8. The method for monitoring drought in alpine grasslands based on localized verification of the Palmer Drought Index according to claim 1, characterized in that: In step 3, the Palmer Drought Index is verified according to the coefficient, as follows: Where X' is the calibrated Palmer Drought Index; X is the Palmer Drought Index; β0 is the intercept; and β1 is the regression coefficient.

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